Analyzing the effects of species gain and loss on ecosystem function using the extended Price equation partition
Bibliographic record
Abstract
In nature species richness and composition, as well as the functioning of individual species, all covary along environmental gradients, making it difficult to tease apart their effects on ecosystem function. Here we use a novel extension of the Price equation to partition the causes of functional variation between any two sites sharing at least one species in common. We use the extension to separate effects of species loss from those of species gain; species gain is analogous to migration in evolution. Previous theoretical and empirical studies of biodiversity and ecosystem function fail to distinguish effects of species gain from those of species loss, and so are conceptually incomplete. Application of this approach to data on total plant biomass along an experimental N enrichment gradient leads to novel empirical insights and reveals subtle effects. For instance, effects of species gain are non‐negligible even though enrichment leads to loss of many species and gain of few, and non‐random gain of high‐biomass species reduces the biomass of the persisting species. We also discuss the interpretation of this new approach, which provides a highly‐general partitioning of the factors affecting ecosystem function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".